Accelerating Real-Time Coupled Cluster Methods with Single-Precision Arithmetic and Adaptive Numerical Integration
Zhe Wang, Benjamin G. Peyton, T. Daniel Crawford

TL;DR
This paper presents a method to accelerate real-time coupled cluster calculations by using single-precision arithmetic, GPU implementation, and adaptive numerical integration, achieving significant speedups without compromising accuracy.
Contribution
It introduces a combined approach of single-precision arithmetic, GPU acceleration, and adaptive integration algorithms to enhance the efficiency of real-time coupled cluster simulations.
Findings
Single-precision reduces storage and computation costs by about half.
GPU implementation speeds up simulations by up to 14 times.
Adaptive integrators balance computational cost and numerical stability.
Abstract
We explore the framework of a real-time coupled cluster method with a focus on improving its computational efficiency. Propagation of the wave function via the time-dependent Schr\"odinger equation places high demands on computing resources, particularly for high level theories such as coupled cluster with polynomial scaling. Similar to earlier investigations of coupled cluster properties, we demonstrate that the use of single-precision arithmetic reduces both the storage and multiplicative costs of the real-time simulation by approximately a factor of two with no significant impact on the resulting UV/vis absorption spectrum computed via the Fourier transform of the time-dependent dipole moment. Additional speedups of up to a factor of 14 in test simulations of water clusters are obtained via a straightforward GPU-based implementation as compared to conventional CPU calculations. We…
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Taxonomy
TopicsMeteorological Phenomena and Simulations · Spectroscopy and Laser Applications · Spectroscopy and Quantum Chemical Studies
